Popularity of a piece of content, e.g., a TV program, in the general population is very useful to a TV content recommendation system when making recommendations to its users, especially those users for which the system has little knowledge. But the process of acquiring first-hand, comprehensive popularity data about the TV programs is often expensive and time-consuming. For example, the universe of content from which to make recommendations is so large that it renders almost any manual approaches intractable. Moreover, it is even more difficult for the system to make accurate recommendations when the system in general and individual devices in particular have initially started operation and have very little, if any, logged data related to TV programs that have been watched in the past. Finally, a successful recommendation system should be able to update the recommended TV programs continuously as their popularity changes over time.